Overview

Dataset statistics

Number of variables81
Number of observations1460
Missing cells6965
Missing cells (%)5.9%
Total size in memory924.0 KiB
Average record size in memory648.1 B

Variable types

Numeric38
Text43

Alerts

LotFrontage has 259 (17.7%) missing valuesMissing
Alley has 1369 (93.8%) missing valuesMissing
BsmtQual has 37 (2.5%) missing valuesMissing
BsmtCond has 37 (2.5%) missing valuesMissing
BsmtExposure has 38 (2.6%) missing valuesMissing
BsmtFinType1 has 37 (2.5%) missing valuesMissing
BsmtFinType2 has 38 (2.6%) missing valuesMissing
FireplaceQu has 690 (47.3%) missing valuesMissing
GarageType has 81 (5.5%) missing valuesMissing
GarageYrBlt has 81 (5.5%) missing valuesMissing
GarageFinish has 81 (5.5%) missing valuesMissing
GarageQual has 81 (5.5%) missing valuesMissing
GarageCond has 81 (5.5%) missing valuesMissing
PoolQC has 1453 (99.5%) missing valuesMissing
Fence has 1179 (80.8%) missing valuesMissing
MiscFeature has 1406 (96.3%) missing valuesMissing
MiscVal is highly skewed (γ1 = 24.47679419)Skewed
Id has unique valuesUnique
MasVnrArea has 861 (59.0%) zerosZeros
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtFinSF2 has 1293 (88.6%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
2ndFlrSF has 829 (56.8%) zerosZeros
LowQualFinSF has 1434 (98.2%) zerosZeros
BsmtFullBath has 856 (58.6%) zerosZeros
BsmtHalfBath has 1378 (94.4%) zerosZeros
HalfBath has 913 (62.5%) zerosZeros
Fireplaces has 690 (47.3%) zerosZeros
GarageCars has 81 (5.5%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
WoodDeckSF has 761 (52.1%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
EnclosedPorch has 1252 (85.8%) zerosZeros
3SsnPorch has 1436 (98.4%) zerosZeros
ScreenPorch has 1344 (92.1%) zerosZeros
PoolArea has 1453 (99.5%) zerosZeros
MiscVal has 1408 (96.4%) zerosZeros

Reproduction

Analysis started2023-10-27 11:24:51.309542
Analysis finished2023-10-27 11:24:52.042081
Duration0.73 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Id
Real number (ℝ)

UNIQUE 

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:52.264772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.6100094
Coefficient of variation (CV)0.577152648
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotonicityStrictly increasing
2023-10-27T13:24:52.346482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
982 1
 
0.1%
980 1
 
0.1%
979 1
 
0.1%
978 1
 
0.1%
977 1
 
0.1%
976 1
 
0.1%
975 1
 
0.1%
974 1
 
0.1%
973 1
 
0.1%
Other values (1450) 1450
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
ValueCountFrequency (%)
1460 1
0.1%
1459 1
0.1%
1458 1
0.1%
1457 1
0.1%
1456 1
0.1%

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726027
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:52.412096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.30057099
Coefficient of variation (CV)0.7434553226
Kurtosis1.580187965
Mean56.89726027
Median Absolute Deviation (MAD)30
Skewness1.407656747
Sum83070
Variance1789.338306
MonotonicityNot monotonic
2023-10-27T13:24:52.471026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 536
36.7%
60 299
20.5%
50 144
 
9.9%
120 87
 
6.0%
30 69
 
4.7%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
20 536
36.7%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
4.3%
120 87
6.0%
90 52
3.6%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:52.522712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.034246575
Min length2

Characters and Unicode

Total characters2970
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL
ValueCountFrequency (%)
rl 1151
78.3%
rm 218
 
14.8%
fv 65
 
4.4%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%
2023-10-27T13:24:52.643823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2910
98.0%
Lowercase Letter 30
 
1.0%
Space Separator 10
 
0.3%
Open Punctuation 10
 
0.3%
Close Punctuation 10
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1385
47.6%
L 1151
39.6%
M 218
 
7.5%
F 65
 
2.2%
V 65
 
2.2%
H 16
 
0.5%
C 10
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
l 20
66.7%
a 10
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2940
99.0%
Common 30
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1385
47.1%
L 1151
39.1%
M 218
 
7.4%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
a 10
 
0.3%
Common
ValueCountFrequency (%)
10
33.3%
( 10
33.3%
) 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotFrontage
Real number (ℝ)

MISSING 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.04995837
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:52.722174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.28475177
Coefficient of variation (CV)0.3466776047
Kurtosis17.45286726
Mean70.04995837
Median Absolute Deviation (MAD)11
Skewness2.163569142
Sum84130
Variance589.7491687
MonotonicityNot monotonic
2023-10-27T13:24:52.796275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (100) 654
44.8%
(Missing) 259
 
17.7%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.82808
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:52.871442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.264932
Coefficient of variation (CV)0.949075601
Kurtosis203.243271
Mean10516.82808
Median Absolute Deviation (MAD)1998
Skewness12.20768785
Sum15354569
Variance99625649.65
MonotonicityNot monotonic
2023-10-27T13:24:52.945362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
0.1%
1477 1
0.1%
1491 1
0.1%
1526 1
0.1%
1533 2
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%

Street
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:52.999313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave
ValueCountFrequency (%)
pave 1454
99.6%
grvl 6
 
0.4%
2023-10-27T13:24:53.137096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4380
75.0%
Uppercase Letter 1460
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1460
33.3%
a 1454
33.2%
e 1454
33.2%
r 6
 
0.1%
l 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 1454
99.6%
G 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5840
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Alley
Text

MISSING 

Distinct2
Distinct (%)2.2%
Missing1369
Missing (%)93.8%
Memory size11.5 KiB
2023-10-27T13:24:53.202460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters364
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrvl
2nd rowPave
3rd rowPave
4th rowGrvl
5th rowPave
ValueCountFrequency (%)
grvl 50
54.9%
pave 41
45.1%
2023-10-27T13:24:53.326576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v 91
25.0%
G 50
13.7%
r 50
13.7%
l 50
13.7%
P 41
11.3%
a 41
11.3%
e 41
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 273
75.0%
Uppercase Letter 91
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 91
33.3%
r 50
18.3%
l 50
18.3%
a 41
15.0%
e 41
15.0%
Uppercase Letter
ValueCountFrequency (%)
G 50
54.9%
P 41
45.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 364
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 91
25.0%
G 50
13.7%
r 50
13.7%
l 50
13.7%
P 41
11.3%
a 41
11.3%
e 41
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 91
25.0%
G 50
13.7%
r 50
13.7%
l 50
13.7%
P 41
11.3%
a 41
11.3%
e 41
11.3%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:53.387915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1
ValueCountFrequency (%)
reg 925
63.4%
ir1 484
33.2%
ir2 41
 
2.8%
ir3 10
 
0.7%
2023-10-27T13:24:53.511263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1995
45.5%
Lowercase Letter 1850
42.2%
Decimal Number 535
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 484
90.5%
2 41
 
7.7%
3 10
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
R 1460
73.2%
I 535
 
26.8%
Lowercase Letter
ValueCountFrequency (%)
e 925
50.0%
g 925
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3845
87.8%
Common 535
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1460
38.0%
e 925
24.1%
g 925
24.1%
I 535
 
13.9%
Common
ValueCountFrequency (%)
1 484
90.5%
2 41
 
7.7%
3 10
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:53.570941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl
ValueCountFrequency (%)
lvl 1311
89.8%
bnk 63
 
4.3%
hls 50
 
3.4%
low 36
 
2.5%
2023-10-27T13:24:53.691059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2820
64.4%
Uppercase Letter 1560
35.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1311
46.5%
l 1311
46.5%
n 63
 
2.2%
k 63
 
2.2%
o 36
 
1.3%
w 36
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
L 1397
89.6%
B 63
 
4.0%
H 50
 
3.2%
S 50
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4380
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:53.767596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub
ValueCountFrequency (%)
allpub 1459
99.9%
nosewa 1
 
0.1%
2023-10-27T13:24:53.898480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5839
66.7%
Uppercase Letter 2921
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 2918
50.0%
u 1459
25.0%
b 1459
25.0%
o 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 1459
49.9%
P 1459
49.9%
N 1
 
< 0.1%
S 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:53.969294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.959589041
Min length3

Characters and Unicode

Total characters8701
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2
ValueCountFrequency (%)
inside 1052
72.1%
corner 263
 
18.0%
culdsac 94
 
6.4%
fr2 47
 
3.2%
fr3 4
 
0.3%
2023-10-27T13:24:54.115740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6951
79.9%
Uppercase Letter 1699
 
19.5%
Decimal Number 51
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1315
18.9%
n 1315
18.9%
s 1052
15.1%
i 1052
15.1%
d 1052
15.1%
r 526
 
7.6%
o 263
 
3.8%
c 94
 
1.4%
a 94
 
1.4%
u 94
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
I 1052
61.9%
C 357
 
21.0%
S 94
 
5.5%
D 94
 
5.5%
F 51
 
3.0%
R 51
 
3.0%
Decimal Number
ValueCountFrequency (%)
2 47
92.2%
3 4
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 8650
99.4%
Common 51
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1315
15.2%
n 1315
15.2%
I 1052
12.2%
s 1052
12.2%
i 1052
12.2%
d 1052
12.2%
r 526
 
6.1%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (7) 572
6.6%
Common
ValueCountFrequency (%)
2 47
92.2%
3 4
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:54.172484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl
ValueCountFrequency (%)
gtl 1382
94.7%
mod 65
 
4.5%
sev 13
 
0.9%
2023-10-27T13:24:54.290417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2920
66.7%
Uppercase Letter 1460
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1382
47.3%
l 1382
47.3%
o 65
 
2.2%
d 65
 
2.2%
e 13
 
0.4%
v 13
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
G 1382
94.7%
M 65
 
4.5%
S 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4380
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%
Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:54.395494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.494520548
Min length5

Characters and Unicode

Total characters9482
Distinct characters38
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge
ValueCountFrequency (%)
names 225
15.4%
collgcr 150
 
10.3%
oldtown 113
 
7.7%
edwards 100
 
6.8%
somerst 86
 
5.9%
gilbert 79
 
5.4%
nridght 77
 
5.3%
sawyer 74
 
5.1%
nwames 73
 
5.0%
sawyerw 59
 
4.0%
Other values (15) 424
29.0%
2023-10-27T13:24:54.668550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6764
71.3%
Uppercase Letter 2718
28.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 931
13.8%
e 905
13.4%
l 622
9.2%
d 506
 
7.5%
s 486
 
7.2%
o 483
 
7.1%
m 439
 
6.5%
w 414
 
6.1%
i 351
 
5.2%
a 345
 
5.1%
Other values (10) 1282
19.0%
Uppercase Letter
ValueCountFrequency (%)
N 425
15.6%
C 407
15.0%
S 352
13.0%
A 298
11.0%
T 188
6.9%
W 157
 
5.8%
O 150
 
5.5%
B 118
 
4.3%
R 115
 
4.2%
E 100
 
3.7%
Other values (8) 408
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9482
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%
Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:54.743384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.121232877
Min length4

Characters and Unicode

Total characters6017
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm 1260
86.3%
feedr 81
 
5.5%
artery 48
 
3.3%
rran 26
 
1.8%
posn 19
 
1.3%
rrae 11
 
0.8%
posa 8
 
0.5%
rrnn 5
 
0.3%
rrne 2
 
0.1%
2023-10-27T13:24:54.884074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4442
73.8%
Uppercase Letter 1575
 
26.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1437
32.4%
o 1287
29.0%
m 1260
28.4%
e 223
 
5.0%
d 81
 
1.8%
t 48
 
1.1%
y 48
 
1.1%
n 31
 
0.7%
s 27
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 1286
81.7%
A 93
 
5.9%
R 88
 
5.6%
F 81
 
5.1%
P 27
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6017
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%
Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:54.951129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.006849315
Min length4

Characters and Unicode

Total characters5850
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm 1445
99.0%
feedr 6
 
0.4%
artery 2
 
0.1%
rrnn 2
 
0.1%
posn 2
 
0.1%
posa 1
 
0.1%
rran 1
 
0.1%
rrae 1
 
0.1%
2023-10-27T13:24:55.091682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4379
74.9%
Uppercase Letter 1471
 
25.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1455
33.2%
o 1448
33.1%
m 1445
33.0%
e 15
 
0.3%
d 6
 
0.1%
n 3
 
0.1%
s 3
 
0.1%
t 2
 
< 0.1%
y 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1449
98.5%
R 8
 
0.5%
F 6
 
0.4%
A 5
 
0.3%
P 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:55.157866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.299315068
Min length4

Characters and Unicode

Total characters6277
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam
ValueCountFrequency (%)
1fam 1220
83.6%
twnhse 114
 
7.8%
duplex 52
 
3.6%
twnhs 43
 
2.9%
2fmcon 31
 
2.1%
2023-10-27T13:24:55.296527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3452
55.0%
Uppercase Letter 1574
25.1%
Decimal Number 1251
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1251
36.2%
a 1220
35.3%
n 188
 
5.4%
w 157
 
4.5%
h 157
 
4.5%
s 157
 
4.5%
l 52
 
1.5%
x 52
 
1.5%
e 52
 
1.5%
p 52
 
1.5%
Other values (3) 114
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
F 1220
77.5%
T 157
 
10.0%
E 114
 
7.2%
D 52
 
3.3%
C 31
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 1220
97.5%
2 31
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5026
80.1%
Common 1251
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1251
24.9%
a 1220
24.3%
F 1220
24.3%
n 188
 
3.7%
T 157
 
3.1%
w 157
 
3.1%
h 157
 
3.1%
s 157
 
3.1%
E 114
 
2.3%
l 52
 
1.0%
Other values (8) 353
 
7.0%
Common
ValueCountFrequency (%)
1 1220
97.5%
2 31
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%
Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:55.373032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.910958904
Min length4

Characters and Unicode

Total characters8630
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story
ValueCountFrequency (%)
1story 726
49.7%
2story 445
30.5%
1.5fin 154
 
10.5%
slvl 65
 
4.5%
sfoyer 37
 
2.5%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.5%
2023-10-27T13:24:55.523947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5336
61.8%
Uppercase Letter 1562
 
18.1%
Decimal Number 1545
 
17.9%
Other Punctuation 187
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1208
22.6%
r 1208
22.6%
y 1208
22.6%
t 1171
21.9%
n 187
 
3.5%
i 162
 
3.0%
v 65
 
1.2%
l 65
 
1.2%
e 37
 
0.7%
f 25
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
S 1273
81.5%
F 199
 
12.7%
L 65
 
4.2%
U 25
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 894
57.9%
2 464
30.0%
5 187
 
12.1%
Other Punctuation
ValueCountFrequency (%)
. 187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6898
79.9%
Common 1732
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1273
18.5%
o 1208
17.5%
r 1208
17.5%
y 1208
17.5%
t 1171
17.0%
F 199
 
2.9%
n 187
 
2.7%
i 162
 
2.3%
L 65
 
0.9%
v 65
 
0.9%
Other values (4) 152
 
2.2%
Common
ValueCountFrequency (%)
1 894
51.6%
2 464
26.8%
5 187
 
10.8%
. 187
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.099315068
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:55.594659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.382996547
Coefficient of variation (CV)0.2267462053
Kurtosis0.09629277836
Mean6.099315068
Median Absolute Deviation (MAD)1
Skewness0.2169439278
Sum8905
Variance1.912679448
MonotonicityNot monotonic
2023-10-27T13:24:55.647688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.575342466
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:55.698678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.112799337
Coefficient of variation (CV)0.1995930014
Kurtosis1.106413461
Mean5.575342466
Median Absolute Deviation (MAD)0
Skewness0.6930674725
Sum8140
Variance1.238322364
MonotonicityNot monotonic
2023-10-27T13:24:55.756117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.2%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.0%
6 252
 
17.3%
5 821
56.2%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.267808
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:55.823421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.20290404
Coefficient of variation (CV)0.01532156307
Kurtosis-0.4395519416
Mean1971.267808
Median Absolute Deviation (MAD)25
Skewness-0.6134611725
Sum2878051
Variance912.2154126
MonotonicityNot monotonic
2023-10-27T13:24:55.898987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
0.3%
1882 1
 
0.1%
1885 2
0.1%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.865753
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:55.973443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.64540681
Coefficient of variation (CV)0.01040141217
Kurtosis-1.272245192
Mean1984.865753
Median Absolute Deviation (MAD)13
Skewness-0.5035620027
Sum2897904
Variance426.2328223
MonotonicityNot monotonic
2023-10-27T13:24:56.049880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:56.115578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.62260274
Min length3

Characters and Unicode

Total characters6749
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable
ValueCountFrequency (%)
gable 1141
78.2%
hip 286
 
19.6%
flat 13
 
0.9%
gambrel 11
 
0.8%
mansard 7
 
0.5%
shed 2
 
0.1%
2023-10-27T13:24:56.257582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5289
78.4%
Uppercase Letter 1460
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1179
22.3%
l 1165
22.0%
e 1154
21.8%
b 1152
21.8%
i 286
 
5.4%
p 286
 
5.4%
r 18
 
0.3%
t 13
 
0.2%
m 11
 
0.2%
d 9
 
0.2%
Other values (3) 16
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
G 1152
78.9%
H 286
 
19.6%
F 13
 
0.9%
M 7
 
0.5%
S 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6749
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%
Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:56.337389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.996575342
Min length4

Characters and Unicode

Total characters10215
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg
ValueCountFrequency (%)
compshg 1434
98.2%
tar&grv 11
 
0.8%
wdshngl 6
 
0.4%
wdshake 5
 
0.3%
metal 1
 
0.1%
membran 1
 
0.1%
roll 1
 
0.1%
clytile 1
 
0.1%
2023-10-27T13:24:56.484303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7287
71.3%
Uppercase Letter 2917
28.6%
Other Punctuation 11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 1445
19.8%
g 1440
19.8%
m 1435
19.7%
o 1435
19.7%
p 1434
19.7%
r 23
 
0.3%
a 18
 
0.2%
l 11
 
0.2%
d 11
 
0.2%
v 11
 
0.2%
Other values (7) 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S 1445
49.5%
C 1435
49.2%
T 12
 
0.4%
W 11
 
0.4%
G 11
 
0.4%
M 2
 
0.1%
R 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
& 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10204
99.9%
Common 11
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1445
14.2%
h 1445
14.2%
g 1440
14.1%
C 1435
14.1%
m 1435
14.1%
o 1435
14.1%
p 1434
14.1%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (14) 82
 
0.8%
Common
ValueCountFrequency (%)
& 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%
Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:56.571433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.979452055
Min length5

Characters and Unicode

Total characters10190
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd 515
30.9%
hdboard 222
13.3%
metalsd 220
13.2%
wd 206
 
12.4%
sdng 206
 
12.4%
plywood 108
 
6.5%
cemntbd 61
 
3.7%
brkface 50
 
3.0%
wdshing 26
 
1.6%
stucco 25
 
1.5%
Other values (6) 27
 
1.6%
2023-10-27T13:24:56.736250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7199
70.6%
Uppercase Letter 2785
 
27.3%
Space Separator 206
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1786
24.8%
l 844
11.7%
n 831
11.5%
y 623
 
8.7%
i 541
 
7.5%
a 492
 
6.8%
o 468
 
6.5%
e 333
 
4.6%
t 309
 
4.3%
r 274
 
3.8%
Other values (10) 698
 
9.7%
Uppercase Letter
ValueCountFrequency (%)
S 1016
36.5%
V 515
18.5%
B 336
 
12.1%
W 232
 
8.3%
H 222
 
8.0%
M 220
 
7.9%
P 108
 
3.9%
C 64
 
2.3%
F 50
 
1.8%
A 21
 
0.8%
Space Separator
ValueCountFrequency (%)
206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9984
98.0%
Common 206
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1786
17.9%
S 1016
10.2%
l 844
 
8.5%
n 831
 
8.3%
y 623
 
6.2%
i 541
 
5.4%
V 515
 
5.2%
a 492
 
4.9%
o 468
 
4.7%
B 336
 
3.4%
Other values (21) 2532
25.4%
Common
ValueCountFrequency (%)
206
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%
Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:56.827135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.973287671
Min length5

Characters and Unicode

Total characters10181
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd 504
29.6%
wd 235
13.8%
metalsd 214
12.6%
hdboard 207
12.2%
sdng 197
 
11.6%
plywood 142
 
8.3%
cmentbd 60
 
3.5%
shng 38
 
2.2%
stucco 26
 
1.5%
brkface 25
 
1.5%
Other values (8) 54
 
3.2%
2023-10-27T13:24:56.993893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7193
70.7%
Uppercase Letter 2746
 
27.0%
Space Separator 242
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1766
24.6%
l 861
12.0%
n 834
11.6%
y 646
 
9.0%
o 523
 
7.3%
i 504
 
7.0%
a 446
 
6.2%
t 316
 
4.4%
e 305
 
4.2%
g 255
 
3.5%
Other values (10) 737
10.2%
Uppercase Letter
ValueCountFrequency (%)
S 1017
37.0%
V 504
18.4%
B 300
 
10.9%
W 235
 
8.6%
M 214
 
7.8%
H 207
 
7.5%
P 142
 
5.2%
C 68
 
2.5%
F 25
 
0.9%
A 23
 
0.8%
Other values (2) 11
 
0.4%
Space Separator
ValueCountFrequency (%)
242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9939
97.6%
Common 242
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1766
17.8%
S 1017
10.2%
l 861
 
8.7%
n 834
 
8.4%
y 646
 
6.5%
o 523
 
5.3%
V 504
 
5.1%
i 504
 
5.1%
a 446
 
4.5%
t 316
 
3.2%
Other values (22) 2522
25.4%
Common
ValueCountFrequency (%)
242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10181
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%
Distinct4
Distinct (%)0.3%
Missing8
Missing (%)0.5%
Memory size11.5 KiB
2023-10-27T13:24:57.066825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length4
Mean length5.028236915
Min length4

Characters and Unicode

Total characters7301
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowNone
3rd rowBrkFace
4th rowNone
5th rowBrkFace
ValueCountFrequency (%)
none 864
59.5%
brkface 445
30.6%
stone 128
 
8.8%
brkcmn 15
 
1.0%
2023-10-27T13:24:57.199299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1437
19.7%
n 1007
13.8%
o 992
13.6%
N 864
11.8%
B 460
 
6.3%
r 460
 
6.3%
k 460
 
6.3%
F 445
 
6.1%
a 445
 
6.1%
c 445
 
6.1%
Other values (4) 286
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5389
73.8%
Uppercase Letter 1912
 
26.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1437
26.7%
n 1007
18.7%
o 992
18.4%
r 460
 
8.5%
k 460
 
8.5%
a 445
 
8.3%
c 445
 
8.3%
t 128
 
2.4%
m 15
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 864
45.2%
B 460
24.1%
F 445
23.3%
S 128
 
6.7%
C 15
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 7301
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1437
19.7%
n 1007
13.8%
o 992
13.6%
N 864
11.8%
B 460
 
6.3%
r 460
 
6.3%
k 460
 
6.3%
F 445
 
6.1%
a 445
 
6.1%
c 445
 
6.1%
Other values (4) 286
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1437
19.7%
n 1007
13.8%
o 992
13.6%
N 864
11.8%
B 460
 
6.3%
r 460
 
6.3%
k 460
 
6.3%
F 445
 
6.1%
a 445
 
6.1%
c 445
 
6.1%
Other values (4) 286
 
3.9%

MasVnrArea
Real number (ℝ)

ZEROS 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.6852617
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:57.279113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.0662066
Coefficient of variation (CV)1.746306115
Kurtosis10.08241732
Mean103.6852617
Median Absolute Deviation (MAD)0
Skewness2.66908421
Sum150551
Variance32784.97117
MonotonicityNot monotonic
2023-10-27T13:24:57.354901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 861
59.0%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
200 6
 
0.4%
Other values (317) 529
36.2%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 861
59.0%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:57.523594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta 906
62.1%
gd 488
33.4%
ex 52
 
3.6%
fa 14
 
1.0%
2023-10-27T13:24:57.648194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2366
81.0%
Lowercase Letter 554
 
19.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 906
38.3%
A 906
38.3%
G 488
20.6%
E 52
 
2.2%
F 14
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
d 488
88.1%
x 52
 
9.4%
a 14
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:57.707223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 1282
87.8%
gd 146
 
10.0%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%
2023-10-27T13:24:57.829223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2742
93.9%
Lowercase Letter 178
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1282
46.8%
A 1282
46.8%
G 146
 
5.3%
F 28
 
1.0%
E 3
 
0.1%
P 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 146
82.0%
a 28
 
15.7%
x 3
 
1.7%
o 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%
Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:57.902759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.515753425
Min length4

Characters and Unicode

Total characters8053
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc
ValueCountFrequency (%)
pconc 647
44.3%
cblock 634
43.4%
brktil 146
 
10.0%
slab 24
 
1.6%
stone 6
 
0.4%
wood 3
 
0.2%
2023-10-27T13:24:58.049557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5166
64.2%
Uppercase Letter 2887
35.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1293
25.0%
c 1281
24.8%
l 804
15.6%
k 780
15.1%
n 653
12.6%
i 146
 
2.8%
r 146
 
2.8%
a 24
 
0.5%
b 24
 
0.5%
t 6
 
0.1%
Other values (2) 9
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 1281
44.4%
B 780
27.0%
P 647
22.4%
T 146
 
5.1%
S 30
 
1.0%
W 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8053
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

BsmtQual
Text

MISSING 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
2023-10-27T13:24:58.122415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta 649
45.6%
gd 618
43.4%
ex 121
 
8.5%
fa 35
 
2.5%
2023-10-27T13:24:58.253298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2072
72.8%
Lowercase Letter 774
 
27.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 649
31.3%
A 649
31.3%
G 618
29.8%
E 121
 
5.8%
F 35
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
d 618
79.8%
x 121
 
15.6%
a 35
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2846
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

BsmtCond
Text

MISSING 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
2023-10-27T13:24:58.311840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA
ValueCountFrequency (%)
ta 1311
92.1%
gd 65
 
4.6%
fa 45
 
3.2%
po 2
 
0.1%
2023-10-27T13:24:58.430619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1311
46.1%
A 1311
46.1%
G 65
 
2.3%
d 65
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2734
96.1%
Lowercase Letter 112
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1311
48.0%
A 1311
48.0%
G 65
 
2.4%
F 45
 
1.6%
P 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
d 65
58.0%
a 45
40.2%
o 2
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2846
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1311
46.1%
A 1311
46.1%
G 65
 
2.3%
d 65
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1311
46.1%
A 1311
46.1%
G 65
 
2.3%
d 65
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

BsmtExposure
Text

MISSING 

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
2023-10-27T13:24:58.482579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2844
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv
ValueCountFrequency (%)
no 953
67.0%
av 221
 
15.5%
gd 134
 
9.4%
mn 114
 
8.0%
2023-10-27T13:24:58.596123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1422
50.0%
Lowercase Letter 1422
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 953
67.0%
A 221
 
15.5%
G 134
 
9.4%
M 114
 
8.0%
Lowercase Letter
ValueCountFrequency (%)
o 953
67.0%
v 221
 
15.5%
d 134
 
9.4%
n 114
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2844
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

BsmtFinType1
Text

MISSING 

Distinct6
Distinct (%)0.4%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
2023-10-27T13:24:58.669979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4269
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ
ValueCountFrequency (%)
unf 430
30.2%
glq 418
29.4%
alq 220
15.5%
blq 148
 
10.4%
rec 133
 
9.3%
lwq 74
 
5.2%
2023-10-27T13:24:58.804459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3069
71.9%
Lowercase Letter 1200
 
28.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 860
28.0%
Q 860
28.0%
U 430
14.0%
G 418
13.6%
A 220
 
7.2%
B 148
 
4.8%
R 133
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
n 430
35.8%
f 430
35.8%
e 133
 
11.1%
c 133
 
11.1%
w 74
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4269
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

BsmtFinSF1
Real number (ℝ)

ZEROS 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.639726
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:58.883536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.0980908
Coefficient of variation (CV)1.028082167
Kurtosis11.11823629
Mean443.639726
Median Absolute Deviation (MAD)383.5
Skewness1.685503072
Sum647714
Variance208025.4685
MonotonicityNot monotonic
2023-10-27T13:24:58.955188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%

BsmtFinType2
Text

MISSING 

Distinct6
Distinct (%)0.4%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
2023-10-27T13:24:59.006341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4266
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf
ValueCountFrequency (%)
unf 1256
88.3%
rec 54
 
3.8%
lwq 46
 
3.2%
blq 33
 
2.3%
alq 19
 
1.3%
glq 14
 
1.0%
2023-10-27T13:24:59.126621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1256
29.4%
n 1256
29.4%
f 1256
29.4%
L 112
 
2.6%
Q 112
 
2.6%
R 54
 
1.3%
e 54
 
1.3%
c 54
 
1.3%
w 46
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2666
62.5%
Uppercase Letter 1600
37.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 1256
78.5%
L 112
 
7.0%
Q 112
 
7.0%
R 54
 
3.4%
B 33
 
2.1%
A 19
 
1.2%
G 14
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
n 1256
47.1%
f 1256
47.1%
e 54
 
2.0%
c 54
 
2.0%
w 46
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4266
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 1256
29.4%
n 1256
29.4%
f 1256
29.4%
L 112
 
2.6%
Q 112
 
2.6%
R 54
 
1.3%
e 54
 
1.3%
c 54
 
1.3%
w 46
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 1256
29.4%
n 1256
29.4%
f 1256
29.4%
L 112
 
2.6%
Q 112
 
2.6%
R 54
 
1.3%
e 54
 
1.3%
c 54
 
1.3%
w 46
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

BsmtFinSF2
Real number (ℝ)

ZEROS 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.54931507
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:59.206856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.3192728
Coefficient of variation (CV)3.465556315
Kurtosis20.11333755
Mean46.54931507
Median Absolute Deviation (MAD)0
Skewness4.255261109
Sum67962
Variance26023.90778
MonotonicityNot monotonic
2023-10-27T13:24:59.283457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1293
88.6%
180 5
 
0.3%
374 3
 
0.2%
551 2
 
0.1%
147 2
 
0.1%
294 2
 
0.1%
391 2
 
0.1%
539 2
 
0.1%
96 2
 
0.1%
480 2
 
0.1%
Other values (134) 145
 
9.9%
ValueCountFrequency (%)
0 1293
88.6%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%

BsmtUnfSF
Real number (ℝ)

ZEROS 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.240411
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:59.359060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.8669553
Coefficient of variation (CV)0.7789765094
Kurtosis0.4749939878
Mean567.240411
Median Absolute Deviation (MAD)288
Skewness0.9202684528
Sum828171
Variance195246.4062
MonotonicityNot monotonic
2023-10-27T13:24:59.432967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%

TotalBsmtSF
Real number (ℝ)

ZEROS 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.429452
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:59.503824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.7053245
Coefficient of variation (CV)0.4148790481
Kurtosis13.25048328
Mean1057.429452
Median Absolute Deviation (MAD)234.5
Skewness1.524254549
Sum1543847
Variance192462.3617
MonotonicityNot monotonic
2023-10-27T13:24:59.576974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:59.637554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.000684932
Min length4

Characters and Unicode

Total characters5841
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA
ValueCountFrequency (%)
gasa 1428
97.8%
gasw 18
 
1.2%
grav 7
 
0.5%
wall 4
 
0.3%
othw 2
 
0.1%
floor 1
 
0.1%
2023-10-27T13:24:59.768305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2933
50.2%
Uppercase Letter 2908
49.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1457
49.7%
s 1446
49.3%
l 9
 
0.3%
r 8
 
0.3%
v 7
 
0.2%
t 2
 
0.1%
h 2
 
0.1%
o 2
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
G 1453
50.0%
A 1428
49.1%
W 24
 
0.8%
O 2
 
0.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5841
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:24:59.829599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx
ValueCountFrequency (%)
ex 741
50.8%
ta 428
29.3%
gd 241
 
16.5%
fa 49
 
3.4%
po 1
 
0.1%
2023-10-27T13:24:59.951566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1888
64.7%
Lowercase Letter 1032
35.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 741
39.2%
T 428
22.7%
A 428
22.7%
G 241
 
12.8%
F 49
 
2.6%
P 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
x 741
71.8%
d 241
 
23.4%
a 49
 
4.7%
o 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:00.003366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 1365
93.5%
n 95
 
6.5%
2023-10-27T13:25:00.113414image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 1365
93.5%
N 95
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1460
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 1365
93.5%
N 95
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 1365
93.5%
N 95
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 1365
93.5%
N 95
 
6.5%
Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
2023-10-27T13:25:00.180788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.998629198
Min length3

Characters and Unicode

Total characters7293
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr
ValueCountFrequency (%)
sbrkr 1334
91.4%
fusea 94
 
6.4%
fusef 27
 
1.9%
fusep 3
 
0.2%
mix 1
 
0.1%
2023-10-27T13:25:00.322310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4376
60.0%
Uppercase Letter 2917
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2668
61.0%
k 1334
30.5%
u 124
 
2.8%
s 124
 
2.8%
e 124
 
2.8%
i 1
 
< 0.1%
x 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 1334
45.7%
B 1334
45.7%
F 151
 
5.2%
A 94
 
3.2%
P 3
 
0.1%
M 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7293
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.626712
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:00.401776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.587738
Coefficient of variation (CV)0.3325123481
Kurtosis5.745841482
Mean1162.626712
Median Absolute Deviation (MAD)234.5
Skewness1.376756622
Sum1697435
Variance149450.0792
MonotonicityNot monotonic
2023-10-27T13:25:00.473292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%

2ndFlrSF
Real number (ℝ)

ZEROS 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.9924658
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:00.544502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.5284359
Coefficient of variation (CV)1.258034335
Kurtosis-0.5534635576
Mean346.9924658
Median Absolute Deviation (MAD)0
Skewness0.8130298163
Sum506609
Variance190557.0753
MonotonicityNot monotonic
2023-10-27T13:25:00.747078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 829
56.8%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
780 5
 
0.3%
Other values (407) 566
38.8%
ValueCountFrequency (%)
0 829
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%

LowQualFinSF
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.844520548
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:00.808015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.62308143
Coefficient of variation (CV)8.319430317
Kurtosis83.23481667
Mean5.844520548
Median Absolute Deviation (MAD)0
Skewness9.011341288
Sum8533
Variance2364.204048
MonotonicityNot monotonic
2023-10-27T13:25:00.873466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1434
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1434
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.463699
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:00.944233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.4803834
Coefficient of variation (CV)0.3467456092
Kurtosis4.895120581
Mean1515.463699
Median Absolute Deviation (MAD)326
Skewness1.366560356
Sum2212577
Variance276129.6334
MonotonicityNot monotonic
2023-10-27T13:25:01.018447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
0.1%
438 1
0.1%
480 1
0.1%
520 1
0.1%
605 1
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%

BsmtFullBath
Real number (ℝ)

ZEROS 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4253424658
Minimum0
Maximum3
Zeros856
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.074840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5189106061
Coefficient of variation (CV)1.219983067
Kurtosis-0.8390982655
Mean0.4253424658
Median Absolute Deviation (MAD)0
Skewness0.5960666097
Sum621
Variance0.2692682171
MonotonicityNot monotonic
2023-10-27T13:25:01.127230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%
ValueCountFrequency (%)
3 1
 
0.1%
2 15
 
1.0%
1 588
40.3%
0 856
58.6%

BsmtHalfBath
Real number (ℝ)

ZEROS 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05753424658
Minimum0
Maximum2
Zeros1378
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.179493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2387526463
Coefficient of variation (CV)4.149748376
Kurtosis16.39664195
Mean0.05753424658
Median Absolute Deviation (MAD)0
Skewness4.103402698
Sum84
Variance0.05700282611
MonotonicityNot monotonic
2023-10-27T13:25:01.238623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%
ValueCountFrequency (%)
2 2
 
0.1%
1 80
 
5.5%
0 1378
94.4%

FullBath
Real number (ℝ)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.565068493
Minimum0
Maximum3
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.293988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5509158013
Coefficient of variation (CV)0.3520074704
Kurtosis-0.8570428213
Mean1.565068493
Median Absolute Deviation (MAD)0
Skewness0.0365615584
Sum2285
Variance0.3035082201
MonotonicityNot monotonic
2023-10-27T13:25:01.348334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%
ValueCountFrequency (%)
0 9
 
0.6%
1 650
44.5%
2 768
52.6%
3 33
 
2.3%
ValueCountFrequency (%)
3 33
 
2.3%
2 768
52.6%
1 650
44.5%
0 9
 
0.6%

HalfBath
Real number (ℝ)

ZEROS 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3828767123
Minimum0
Maximum2
Zeros913
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.401226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5028853811
Coefficient of variation (CV)1.313439457
Kurtosis-1.076927284
Mean0.3828767123
Median Absolute Deviation (MAD)0
Skewness0.6758974482
Sum559
Variance0.2528937065
MonotonicityNot monotonic
2023-10-27T13:25:01.458131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%
ValueCountFrequency (%)
2 12
 
0.8%
1 535
36.6%
0 913
62.5%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866438356
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.514878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8157780441
Coefficient of variation (CV)0.2845964025
Kurtosis2.230874582
Mean2.866438356
Median Absolute Deviation (MAD)0
Skewness0.2117900963
Sum4185
Variance0.6654938173
MonotonicityNot monotonic
2023-10-27T13:25:01.573391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 804
55.1%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 804
55.1%
4 213
 
14.6%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 804
55.1%

KitchenAbvGr
Real number (ℝ)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.046575342
Minimum0
Maximum3
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.627234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2203381984
Coefficient of variation (CV)0.2105325718
Kurtosis21.53240384
Mean1.046575342
Median Absolute Deviation (MAD)0
Skewness4.488396777
Sum1528
Variance0.04854892167
MonotonicityNot monotonic
2023-10-27T13:25:01.682263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
ValueCountFrequency (%)
3 2
 
0.1%
2 65
 
4.5%
1 1392
95.3%
0 1
 
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.738420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd
ValueCountFrequency (%)
ta 735
50.3%
gd 586
40.1%
ex 100
 
6.8%
fa 39
 
2.7%
2023-10-27T13:25:01.866492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2195
75.2%
Lowercase Letter 725
 
24.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 735
33.5%
A 735
33.5%
G 586
26.7%
E 100
 
4.6%
F 39
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
d 586
80.8%
x 100
 
13.8%
a 39
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.517808219
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:01.934649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.625393291
Coefficient of variation (CV)0.2493772808
Kurtosis0.8807615657
Mean6.517808219
Median Absolute Deviation (MAD)1
Skewness0.6763408364
Sum9516
Variance2.641903349
MonotonicityNot monotonic
2023-10-27T13:25:01.995759image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 402
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 402
27.5%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
3.2%
9 75
5.1%
Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:02.048082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.057534247
Min length3

Characters and Unicode

Total characters4464
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp
ValueCountFrequency (%)
typ 1360
93.2%
min2 34
 
2.3%
min1 31
 
2.1%
mod 15
 
1.0%
maj1 14
 
1.0%
maj2 5
 
0.3%
sev 1
 
0.1%
2023-10-27T13:25:02.177205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1360
30.5%
y 1360
30.5%
p 1360
30.5%
M 99
 
2.2%
i 65
 
1.5%
n 65
 
1.5%
1 45
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2920
65.4%
Uppercase Letter 1460
32.7%
Decimal Number 84
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 1360
46.6%
p 1360
46.6%
i 65
 
2.2%
n 65
 
2.2%
a 19
 
0.7%
j 19
 
0.7%
o 15
 
0.5%
d 15
 
0.5%
e 1
 
< 0.1%
v 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 1360
93.2%
M 99
 
6.8%
S 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 45
53.6%
2 39
46.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4380
98.1%
Common 84
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1360
31.1%
y 1360
31.1%
p 1360
31.1%
M 99
 
2.3%
i 65
 
1.5%
n 65
 
1.5%
a 19
 
0.4%
j 19
 
0.4%
o 15
 
0.3%
d 15
 
0.3%
Other values (3) 3
 
0.1%
Common
ValueCountFrequency (%)
1 45
53.6%
2 39
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1360
30.5%
y 1360
30.5%
p 1360
30.5%
M 99
 
2.2%
i 65
 
1.5%
n 65
 
1.5%
1 45
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 33
 
0.7%

Fireplaces
Real number (ℝ)

ZEROS 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6130136986
Minimum0
Maximum3
Zeros690
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:02.243161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6446663863
Coefficient of variation (CV)1.051634552
Kurtosis-0.2172372075
Mean0.6130136986
Median Absolute Deviation (MAD)1
Skewness0.6495651831
Sum895
Variance0.4155947496
MonotonicityNot monotonic
2023-10-27T13:25:02.298006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%
ValueCountFrequency (%)
3 5
 
0.3%
2 115
 
7.9%
1 650
44.5%
0 690
47.3%

FireplaceQu
Text

MISSING 

Distinct5
Distinct (%)0.6%
Missing690
Missing (%)47.3%
Memory size11.5 KiB
2023-10-27T13:25:02.353654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1540
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
gd 380
49.4%
ta 313
40.6%
fa 33
 
4.3%
ex 24
 
3.1%
po 20
 
2.6%
2023-10-27T13:25:02.478066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 380
24.7%
d 380
24.7%
T 313
20.3%
A 313
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1083
70.3%
Lowercase Letter 457
29.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 380
35.1%
T 313
28.9%
A 313
28.9%
F 33
 
3.0%
E 24
 
2.2%
P 20
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
d 380
83.2%
a 33
 
7.2%
x 24
 
5.3%
o 20
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 380
24.7%
d 380
24.7%
T 313
20.3%
A 313
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 380
24.7%
d 380
24.7%
T 313
20.3%
A 313
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

GarageType
Text

MISSING 

Distinct6
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2023-10-27T13:25:02.552268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.084118927
Min length6

Characters and Unicode

Total characters8390
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd
ValueCountFrequency (%)
attchd 870
63.1%
detchd 387
28.1%
builtin 88
 
6.4%
basment 19
 
1.4%
carport 9
 
0.7%
2types 6
 
0.4%
2023-10-27T13:25:02.690309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2243
26.7%
c 1257
15.0%
h 1257
15.0%
d 1257
15.0%
A 870
 
10.4%
e 412
 
4.9%
D 387
 
4.6%
n 107
 
1.3%
B 107
 
1.3%
u 88
 
1.0%
Other values (14) 405
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6908
82.3%
Uppercase Letter 1476
 
17.6%
Decimal Number 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2243
32.5%
c 1257
18.2%
h 1257
18.2%
d 1257
18.2%
e 412
 
6.0%
n 107
 
1.5%
u 88
 
1.3%
i 88
 
1.3%
l 88
 
1.3%
a 28
 
0.4%
Other values (6) 83
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
A 870
58.9%
D 387
26.2%
B 107
 
7.2%
I 88
 
6.0%
C 9
 
0.6%
P 9
 
0.6%
T 6
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8384
99.9%
Common 6
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2243
26.8%
c 1257
15.0%
h 1257
15.0%
d 1257
15.0%
A 870
 
10.4%
e 412
 
4.9%
D 387
 
4.6%
n 107
 
1.3%
B 107
 
1.3%
u 88
 
1.0%
Other values (13) 399
 
4.8%
Common
ValueCountFrequency (%)
2 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2243
26.7%
c 1257
15.0%
h 1257
15.0%
d 1257
15.0%
A 870
 
10.4%
e 412
 
4.9%
D 387
 
4.6%
n 107
 
1.3%
B 107
 
1.3%
u 88
 
1.0%
Other values (14) 405
 
4.8%

GarageYrBlt
Real number (ℝ)

MISSING 

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.506164
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:02.777265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.68972477
Coefficient of variation (CV)0.01247897288
Kurtosis-0.418340998
Mean1978.506164
Median Absolute Deviation (MAD)21
Skewness-0.6494146239
Sum2728360
Variance609.5825091
MonotonicityNot monotonic
2023-10-27T13:25:02.854497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 65
 
4.5%
2006 59
 
4.0%
2004 53
 
3.6%
2003 50
 
3.4%
2007 49
 
3.4%
1977 35
 
2.4%
1998 31
 
2.1%
1999 30
 
2.1%
1976 29
 
2.0%
2008 29
 
2.0%
Other values (87) 949
65.0%
(Missing) 81
 
5.5%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
0.2%
1914 2
0.1%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 29
2.0%
2007 49
3.4%
2006 59
4.0%

GarageFinish
Text

MISSING 

Distinct3
Distinct (%)0.2%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2023-10-27T13:25:02.912676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4137
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn
ValueCountFrequency (%)
unf 605
43.9%
rfn 422
30.6%
fin 352
25.5%
2023-10-27T13:25:03.039401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1379
33.3%
F 774
18.7%
U 605
14.6%
f 605
14.6%
R 422
 
10.2%
i 352
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2336
56.5%
Uppercase Letter 1801
43.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1379
59.0%
f 605
25.9%
i 352
 
15.1%
Uppercase Letter
ValueCountFrequency (%)
F 774
43.0%
U 605
33.6%
R 422
23.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4137
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1379
33.3%
F 774
18.7%
U 605
14.6%
f 605
14.6%
R 422
 
10.2%
i 352
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1379
33.3%
F 774
18.7%
U 605
14.6%
f 605
14.6%
R 422
 
10.2%
i 352
 
8.5%

GarageCars
Real number (ℝ)

ZEROS 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.767123288
Minimum0
Maximum4
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:03.107293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7473150101
Coefficient of variation (CV)0.4228991918
Kurtosis0.220997764
Mean1.767123288
Median Absolute Deviation (MAD)0
Skewness-0.3425489297
Sum2580
Variance0.5584797243
MonotonicityNot monotonic
2023-10-27T13:25:03.165269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%
ValueCountFrequency (%)
0 81
 
5.5%
1 369
25.3%
2 824
56.4%
3 181
 
12.4%
4 5
 
0.3%
ValueCountFrequency (%)
4 5
 
0.3%
3 181
 
12.4%
2 824
56.4%
1 369
25.3%
0 81
 
5.5%

GarageArea
Real number (ℝ)

ZEROS 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.980137
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:03.232012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.8048415
Coefficient of variation (CV)0.452037675
Kurtosis0.9170672023
Mean472.980137
Median Absolute Deviation (MAD)120
Skewness0.1799809067
Sum690551
Variance45712.51023
MonotonicityNot monotonic
2023-10-27T13:25:03.306716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%

GarageQual
Text

MISSING 

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2023-10-27T13:25:03.359349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2758
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 1311
95.1%
fa 48
 
3.5%
gd 14
 
1.0%
ex 3
 
0.2%
po 3
 
0.2%
2023-10-27T13:25:03.477018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1311
47.5%
A 1311
47.5%
F 48
 
1.7%
a 48
 
1.7%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2690
97.5%
Lowercase Letter 68
 
2.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1311
48.7%
A 1311
48.7%
F 48
 
1.8%
G 14
 
0.5%
E 3
 
0.1%
P 3
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 48
70.6%
d 14
 
20.6%
x 3
 
4.4%
o 3
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2758
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1311
47.5%
A 1311
47.5%
F 48
 
1.7%
a 48
 
1.7%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1311
47.5%
A 1311
47.5%
F 48
 
1.7%
a 48
 
1.7%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

GarageCond
Text

MISSING 

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
2023-10-27T13:25:03.533810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2758
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 1326
96.2%
fa 35
 
2.5%
gd 9
 
0.7%
po 7
 
0.5%
ex 2
 
0.1%
2023-10-27T13:25:03.651491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1326
48.1%
A 1326
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2705
98.1%
Lowercase Letter 53
 
1.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1326
49.0%
A 1326
49.0%
F 35
 
1.3%
G 9
 
0.3%
P 7
 
0.3%
E 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 35
66.0%
d 9
 
17.0%
o 7
 
13.2%
x 2
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2758
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1326
48.1%
A 1326
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1326
48.1%
A 1326
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:03.702274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 1340
91.8%
n 90
 
6.2%
p 30
 
2.1%
2023-10-27T13:25:03.812553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1460
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

WoodDeckSF
Real number (ℝ)

ZEROS 

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.24452055
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:03.893056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.3387944
Coefficient of variation (CV)1.329931901
Kurtosis2.992950925
Mean94.24452055
Median Absolute Deviation (MAD)0
Skewness1.541375757
Sum137597
Variance15709.81337
MonotonicityNot monotonic
2023-10-27T13:25:03.965195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 761
52.1%
192 38
 
2.6%
100 36
 
2.5%
144 33
 
2.3%
120 31
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
208 10
 
0.7%
240 10
 
0.7%
Other values (264) 484
33.2%
ValueCountFrequency (%)
0 761
52.1%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%

OpenPorchSF
Real number (ℝ)

ZEROS 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.66027397
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:04.033416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.25602768
Coefficient of variation (CV)1.419966538
Kurtosis8.490335806
Mean46.66027397
Median Absolute Deviation (MAD)25
Skewness2.36434174
Sum68124
Variance4389.861203
MonotonicityNot monotonic
2023-10-27T13:25:04.105974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
36 29
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%

EnclosedPorch
Real number (ℝ)

ZEROS 

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95410959
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:04.176531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.1191486
Coefficient of variation (CV)2.783950237
Kurtosis10.43076594
Mean21.95410959
Median Absolute Deviation (MAD)0
Skewness3.089871904
Sum32053
Variance3735.550326
MonotonicityNot monotonic
2023-10-27T13:25:04.248370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1252
85.8%
112 15
 
1.0%
96 6
 
0.4%
192 5
 
0.3%
144 5
 
0.3%
120 5
 
0.3%
216 5
 
0.3%
156 4
 
0.3%
116 4
 
0.3%
252 4
 
0.3%
Other values (110) 155
 
10.6%
ValueCountFrequency (%)
0 1252
85.8%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%

3SsnPorch
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589041
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:04.312889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.31733056
Coefficient of variation (CV)8.598493896
Kurtosis123.6623794
Mean3.409589041
Median Absolute Deviation (MAD)0
Skewness10.30434203
Sum4978
Variance859.505871
MonotonicityNot monotonic
2023-10-27T13:25:04.525141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1436
98.4%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1436
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%

ScreenPorch
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0609589
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:04.594843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.75741528
Coefficient of variation (CV)3.70211589
Kurtosis18.43906784
Mean15.0609589
Median Absolute Deviation (MAD)0
Skewness4.122213743
Sum21989
Variance3108.889359
MonotonicityNot monotonic
2023-10-27T13:25:04.672595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1344
92.1%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1344
92.1%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%

PoolArea
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75890411
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:04.731877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.17730694
Coefficient of variation (CV)14.56277759
Kurtosis223.2684989
Mean2.75890411
Median Absolute Deviation (MAD)0
Skewness14.82837364
Sum4028
Variance1614.215993
MonotonicityNot monotonic
2023-10-27T13:25:04.790984image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1453
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1453
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
ValueCountFrequency (%)
738 1
0.1%
648 1
0.1%
576 1
0.1%
555 1
0.1%
519 1
0.1%

PoolQC
Text

MISSING 

Distinct3
Distinct (%)42.9%
Missing1453
Missing (%)99.5%
Memory size11.5 KiB
2023-10-27T13:25:04.845440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowFa
3rd rowGd
4th rowEx
5th rowGd
ValueCountFrequency (%)
gd 3
42.9%
ex 2
28.6%
fa 2
28.6%
2023-10-27T13:25:04.969823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 3
21.4%
d 3
21.4%
E 2
14.3%
x 2
14.3%
F 2
14.3%
a 2
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
50.0%
Lowercase Letter 7
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 3
42.9%
E 2
28.6%
F 2
28.6%
Lowercase Letter
ValueCountFrequency (%)
d 3
42.9%
x 2
28.6%
a 2
28.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 14
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 3
21.4%
d 3
21.4%
E 2
14.3%
x 2
14.3%
F 2
14.3%
a 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 3
21.4%
d 3
21.4%
E 2
14.3%
x 2
14.3%
F 2
14.3%
a 2
14.3%

Fence
Text

MISSING 

Distinct4
Distinct (%)1.4%
Missing1179
Missing (%)80.8%
Memory size11.5 KiB
2023-10-27T13:25:05.038248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.768683274
Min length4

Characters and Unicode

Total characters1340
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMnPrv
2nd rowGdWo
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv
ValueCountFrequency (%)
mnprv 157
55.9%
gdprv 59
 
21.0%
gdwo 54
 
19.2%
mnww 11
 
3.9%
2023-10-27T13:25:05.167453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 216
16.1%
r 216
16.1%
v 216
16.1%
M 168
12.5%
n 168
12.5%
G 113
8.4%
d 113
8.4%
W 65
 
4.9%
o 54
 
4.0%
w 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 778
58.1%
Uppercase Letter 562
41.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 216
27.8%
v 216
27.8%
n 168
21.6%
d 113
14.5%
o 54
 
6.9%
w 11
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
P 216
38.4%
M 168
29.9%
G 113
20.1%
W 65
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1340
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 216
16.1%
r 216
16.1%
v 216
16.1%
M 168
12.5%
n 168
12.5%
G 113
8.4%
d 113
8.4%
W 65
 
4.9%
o 54
 
4.0%
w 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 216
16.1%
r 216
16.1%
v 216
16.1%
M 168
12.5%
n 168
12.5%
G 113
8.4%
d 113
8.4%
W 65
 
4.9%
o 54
 
4.0%
w 11
 
0.8%

MiscFeature
Text

MISSING 

Distinct4
Distinct (%)7.4%
Missing1406
Missing (%)96.3%
Memory size11.5 KiB
2023-10-27T13:25:05.231498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters216
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rowShed
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed
ValueCountFrequency (%)
shed 49
90.7%
gar2 2
 
3.7%
othr 2
 
3.7%
tenc 1
 
1.9%
2023-10-27T13:25:05.354744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 51
23.6%
e 50
23.1%
S 49
22.7%
d 49
22.7%
r 4
 
1.9%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 159
73.6%
Uppercase Letter 55
 
25.5%
Decimal Number 2
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 51
32.1%
e 50
31.4%
d 49
30.8%
r 4
 
2.5%
a 2
 
1.3%
t 2
 
1.3%
n 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 49
89.1%
G 2
 
3.6%
O 2
 
3.6%
T 1
 
1.8%
C 1
 
1.8%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 214
99.1%
Common 2
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 51
23.8%
e 50
23.4%
S 49
22.9%
d 49
22.9%
r 4
 
1.9%
G 2
 
0.9%
a 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
T 1
 
0.5%
Other values (2) 2
 
0.9%
Common
ValueCountFrequency (%)
2 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 51
23.6%
e 50
23.1%
S 49
22.7%
d 49
22.7%
r 4
 
1.9%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

MiscVal
Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.4890411
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:05.425557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.1230245
Coefficient of variation (CV)11.408001
Kurtosis701.0033423
Mean43.4890411
Median Absolute Deviation (MAD)0
Skewness24.47679419
Sum63494
Variance246138.0554
MonotonicityNot monotonic
2023-10-27T13:25:05.491171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1408
96.4%
400 11
 
0.8%
500 8
 
0.5%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
15500 1
 
0.1%
Other values (11) 11
 
0.8%
ValueCountFrequency (%)
0 1408
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 1
 
0.1%
2000 4
0.3%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.321917808
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:05.553463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.703626208
Coefficient of variation (CV)0.4276591836
Kurtosis-0.4041093415
Mean6.321917808
Median Absolute Deviation (MAD)2
Skewness0.2120529851
Sum9230
Variance7.309594675
MonotonicityNot monotonic
2023-10-27T13:25:05.610602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 253
17.3%
7 234
16.0%
5 204
14.0%
4 141
9.7%
8 122
8.4%
3 106
7.3%
10 89
 
6.1%
11 79
 
5.4%
9 63
 
4.3%
12 59
 
4.0%
Other values (2) 110
7.5%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 106
7.3%
4 141
9.7%
5 204
14.0%
ValueCountFrequency (%)
12 59
4.0%
11 79
5.4%
10 89
6.1%
9 63
4.3%
8 122
8.4%

YrSold
Real number (ℝ)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.815753
Minimum2006
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:05.664330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12007
median2008
Q32009
95-th percentile2010
Maximum2010
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.328095121
Coefficient of variation (CV)0.0006614626458
Kurtosis-1.190600571
Mean2007.815753
Median Absolute Deviation (MAD)1
Skewness0.09626851387
Sum2931411
Variance1.763836649
MonotonicityNot monotonic
2023-10-27T13:25:05.725450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%
ValueCountFrequency (%)
2006 314
21.5%
2007 329
22.5%
2008 304
20.8%
2009 338
23.2%
2010 175
12.0%
ValueCountFrequency (%)
2010 175
12.0%
2009 338
23.2%
2008 304
20.8%
2007 329
22.5%
2006 314
21.5%
Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:05.776386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.158219178
Min length2

Characters and Unicode

Total characters3151
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD
ValueCountFrequency (%)
wd 1267
86.8%
new 122
 
8.4%
cod 43
 
2.9%
conld 9
 
0.6%
conli 5
 
0.3%
conlw 5
 
0.3%
cwd 4
 
0.3%
oth 3
 
0.2%
con 2
 
0.1%
2023-10-27T13:25:05.910329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 1323
42.0%
W 1271
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2854
90.6%
Lowercase Letter 297
 
9.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1323
46.4%
W 1271
44.5%
N 122
 
4.3%
C 68
 
2.4%
O 46
 
1.6%
L 19
 
0.7%
I 5
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
w 127
42.8%
e 122
41.1%
o 21
 
7.1%
n 21
 
7.1%
t 3
 
1.0%
h 3
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3151
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1323
42.0%
W 1271
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1323
42.0%
W 1271
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%
Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:06.000402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.157534247
Min length6

Characters and Unicode

Total characters8990
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal
ValueCountFrequency (%)
normal 1198
82.1%
partial 125
 
8.6%
abnorml 101
 
6.9%
family 20
 
1.4%
alloca 12
 
0.8%
adjland 4
 
0.3%
2023-10-27T13:25:06.138387image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1484
16.5%
l 1468
16.3%
r 1424
15.8%
m 1319
14.7%
o 1311
14.6%
N 1198
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7526
83.7%
Uppercase Letter 1464
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1484
19.7%
l 1468
19.5%
r 1424
18.9%
m 1319
17.5%
o 1311
17.4%
i 145
 
1.9%
t 125
 
1.7%
n 105
 
1.4%
b 101
 
1.3%
y 20
 
0.3%
Other values (3) 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 1198
81.8%
P 125
 
8.5%
A 117
 
8.0%
F 20
 
1.4%
L 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 8990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1484
16.5%
l 1468
16.3%
r 1424
15.8%
m 1319
14.7%
o 1311
14.6%
N 1198
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1484
16.5%
l 1468
16.3%
r 1424
15.8%
m 1319
14.7%
o 1311
14.6%
N 1198
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

SalePrice
Real number (ℝ)

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-27T13:25:06.218267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotonicityNot monotonic
2023-10-27T13:25:06.295181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%